Transcriptome Differential Expression Analysis
Transcriptome Differential Expression Analysis - Related Videos
10:32
Transcriptomic Analysis of Human Retinal Surgical Specimens Using jouRNAl
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Cited by 6 •
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We used retinal samples from retinectomy for a transcriptomic analysis of retinal detachment. We developed a procedure that allows RNA conservation between the surgical blocks and the laboratory. We standardized a protocol to purify RNA by cesium chloride ultracentrifugation to assure that the purified RNAs are suitable for microarray analysis.
13:51
Analyzing Gene Expression from Marine Microbial Communities using Environmental Transcriptomics
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We present a method for generating cDNA from environmental mRNA. In general, total RNA is first collected from the environment, rRNA is selectively removed, mRNA is selectively amplified, and cDNA synthesized from the enriched mRNA pool is sequenced. Recovered sequences can be annotated using standard bioinformatics techniques to identify the expressed genes.
07:27
Transcriptome Analysis of Single Cells
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Cited by 72 •
2011
In this article we describe a simple method for the harvesting of single cells from rat primary neuronal cultures and subsequent transcriptome analysis using aRNA amplification. This approach is generalizable to any cell type.
10:19
Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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Cited by 8 •
2017
Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.
18:30
RNA-seq Analysis of Transcriptomes in Thrombin-treated and Control Human Pulmonary Microvascular Endothelial Cells
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Cited by 12 •
2013
This protocol presents a complete and detailed procedure to apply RNA-seq, a powerful next-generation DNA sequencing technology, to profile transcriptomes in human pulmonary microvascular endothelial cells with or without thrombin treatment. This protocol is generalizable to various cells or tissues affected by different reagents or disease states.
FAQs
Statistical models evaluate whether observed differences in RNA abundance are consistent with variation between biological conditions rather than random fluctuation. Because thousands of genes may be tested simultaneously, multiple-testing control limits the number of apparent findings that arise by chance. This produces a more reliable set of differentially expressed genes for pathway interpretation and follow-up studies.
Normalization adjusts RNA count data so comparisons are not driven primarily by differences in sequencing depth between samples. Without this adjustment, a sample with more sequenced material could appear to have higher RNA abundance broadly across genes. By accounting for this technical factor, the analysis focuses more appropriately on changes associated with the biological conditions.
Alignment places sequenced RNA reads in relation to a reference transcriptome, whereas quantification summarizes how much read evidence supports particular transcripts or genes. These operations prepare the data for downstream comparisons, but they address different computational needs. Choosing how reads are aligned or quantified affects the representation of RNA abundance that statistical models subsequently analyze.
The analysis provides an intermediate view between genetic information and observable traits by showing how gene activity changes across conditions. When DNA variation or regulatory differences alter gene expression, the resulting RNA patterns may help explain developmental, disease-related, or environmental phenotypes. These findings can guide functional studies that investigate whether candidate genes contribute to the observed trait.
A typical workflow organizes RNA sequencing data by relating reads to a reference transcriptome, generating abundance measurements, and normalizing those measurements across samples. Statistical models then compare the biological conditions while accounting for multiple testing. The final output is a prioritized group of genes whose RNA abundance differs, suitable for pathway analysis or further genetic investigation.
The gene list can indicate activated pathways and reveal regulatory responses associated with development, disease, or environmental change. It may also nominate candidate biomarkers and support studies of disease mechanisms. In genetics, these results help interpret genomic data by connecting changes in gene regulation with functional consequences that can be examined in later experiments.